Assignment 3
Process Improvement and Process Control Analysis, and a Quantitative, Computational Solution: Control Charts for Variables and Attributes.
Author: Hardik Bharatkumar Gala
Class code: IE673 live
Date: October 28, 2011
eLearning Pack ID : IE 673-Fall 2011-60-35
Statement: All contents of this assignment were submitted by Hardik Gala
Updated Notes:
Dr. Paul G. Ranky’s Comment: I have enjoyed your Assignment 3, nevertheless I would like to ask you to put more work into the analysis of the results. Please read the syllabus again, very carefully and explain your results in more depths.
At present 6/10
Grade: 6/10
Action Taken by Me: I have updated this assignment with more analysis of results.
Contents:
· Introduction and objectives
· Brief description of methodologies used
· Main body of project
o Developing control chart for variables
o Developing control chart for attributes
o Collaboration
o Analysis of what I have calculated
· Summary
· Further work needed/proposed
· References
· Introduction and objectives:
RNR is an environment friendly company which aims at reducing pollution by finding ways to reuse and recycle plastic and metal alloys. We recycle plastic and metals in a very efficient manner which produces least amount of pollution. We recycle plastic, metals and the alloys in such a manner that the cost is very less compared to producing it the original way that is from the ore and then going through a series of processes. This method not only reduces the cost but also reduces pollution during the preparation process and reduces waste disposal.
The objective of the assignment is to improve an area in the company using process control charts. Statistical process control charts help to establish mistake proof processes, develop upstream process controls, contribute to process and product redesign, and as a result, help to monitor, and keep the processes under continuous control. One process control chart is prepared for variables and two are prepared for attributes. Using these charts solution is found to convert the out of control process into a controlled one.
· Brief description of methodologies used
Statistical process control charts are effective means of controlling variation and obtaining process knowledge. This process knowledge helps to establish mistake proofing activities, develop upstream process controls, contribute to process and product redesign, and, according to Boeing, eventually alleviate the reliance on control charts. Control charts in statistical process control are tools used to determine whether or not a manufacturing or business process is in a state of statistical control.
Control chart for variables:
When dealing with control charts for variables, plotted values on charts represent the following:
• The Mean value of the quality characteristic, over several
samples, to plot the Xbar-chart.
• The Variability with the range, or the standard deviation of a
sample of n units to plot the R- or the S-chart.
By studying the mean value and the variability, we can detect assignable causes of nonconformity
Control chart for attributes:
The goal of control charts for a variable is to control mean and variability of a process but in the case of control charts for attributes we focus on the number of nonconforming units / nonconformities in a population.
There are three types of control charts for attributes. Their use depends on which quality characteristic we want to measure or control, on how many there are to examine, and the characteristic of controls, meaning constant or variable sample size:
• The p-chart: it is a control chart for the percentage of nonconformance
• The c-chart: it is a control chart for the number of defects or nonconformities
• The u-chart: it is a control chart for the number of nonconformities per unit
· Main body of project
o Developing control chart for variables
The construction of a control chart is typically performed in three main steps:
1. Collect data
2. Plot data
3. Compute the central line and control limits
After the above steps are performed, the chart is analyzed to find any process that is out of control. The cause and effects are analyzed and corrective measures are suggested.
R chart has to be analyzed first followed by the X bar chart. If no points are out of limits we have to check the following two rules:
§ Are there 7 consecutive points above or below the central line?
§ Are 7 consecutive points increasing, or decreasing?
This step detects nonrandom progressions and shows that the process is out of control. If the chart shows, that the points are not out control, then we can state, that the process is in control.
RNR recycles plastic. The most important thing to be known while recycling plastic is the heating time. The plastic has to be heated for a specified time and then it has to be molded back into usable form. So in this control chart 5 random products are reviewed over a period of 25 days. Thus the sample size is 5 and number of samples is 25. Using this we calculate the center line and upper and lower limits for both the X bar chart and R chart.
|
Coefficients: |
A2 |
0.577 |
|
|
D4 |
2.114 |
||
|
D3 |
0 |
||
CONTROL CHART FOR VARIABLES (CLICK HERE TO SEE THE CHART)
Analysis of the control charts:
Analysis of the R Chart - None of the points are out of limit and there are no seven consecutive points above or below the central line. Thus the process is under control.
Analysis of the X-Bar Chart - None of the points are out of limit and there are no seven consecutive points above or below the central line. Thus the process is under control.
Thus both the charts satisfy the required criteria and none of the process is out of control. Thus the control chart helps in finding any defects in the process and helps us in finding them out.
o Developing control chart for attributes
When quality control focuses on a quality characteristic which is difficult or expensive to quantify, the control chart for attributes is a useful alternative. Attribute data is go / no go, yes / no, or good / bad. Attribute data tracks conformance vs. nonconformance. It is used when classifying defect.
Attributes that concern quality characteristic can be classified as either
• conforming, or
• nonconforming to specifications.
Nonconformance means that the unit tested is not conforming to standards regarding one or more of examined quality characteristics.
The goal of control charts for a variable is to control mean and variability of a process but in the case of control charts for attributes we focus on the number of nonconforming units/nonconformities in a population.
CONTROL CHART FOR ATTRIBUTES : P-CHART(CLICK HERE TO SEE THE CHART)
RNR uses p chart to identify any defective products in the batch. This is done before supplying to the customer. This helps us in building customer satisfaction.
Analysis of p chart: The chart is plotted with a sample size of 500. The control limits and central lines are calculated using the formulas. There are no samples which go above the control limit. There are no 7 consecutive points above or below the bar values. Thus the process is in control. Thus none of the product is found defective and it can be supplied to the customer.
CONTROL CHART FOR ATTRIBUTES : C-CHART(CLICK HERE TO SEE THE CHART)
C chart is used to calculate the number of non-conformities per unit. It is used in quality control when sample size is constant.
Analysis of C chart: RNR works closely with its collaborating companies and has most of its raw materials are supplied by these collaborating companies. So basically, the main suppliers for the company are the collaborating companies. So the company analyzes the nonconformities per unit received.
The first chart shows a process which is out of control. There are two samples which go out of control viz. 21 and 25. These processes go out of control.
Steps taken to bring process under control- CAPA: Since the process is out of control it has to be brought in control. After looking into the matter it was found that 21st and 25th samples are defective. These samples should not be sent to the customers. Instead they have to be corrected. The quality control teams now looks into this matter and finds some solutions to ensure that it does not happen again. They come up with a Corrective and Preventive actions.
Corrective and Preventive actions: After investigations the quality control team has found the cause and come up with a solution. The cooling time was the problem. Thus the cooling time has to be maintained properly to avoid this again. If this is not followed this problem will occur again. The products manufactured using this method if sent to the customers will lead to dissatisfaction thus ruining the image of the company. The procedure has to be changed to improve the product quality. After rejecting this batch, the chart has been re-worked on. From the chart it is clear that the process is in control.
Analysis of reworked C-chart: In the second chart one of the points is above the control limits. There are no 7 consecutive points as well. Thus the process is now under control.
o Collaboration
RNR is in collaboration with Contrinex, Mitsui Seiki, Trans Tech and Wild Republic. RNR will efficiently collaborate with these companies and produce ecofriendly products.
Contrinex: Contrinex is a leading manufacturer of inductive and photoelectric sensors. Stainless steel is mainly used in the production of sensors. This company produces a lot of scrap stainless steel. Thus this steel can be used at RNR to recycle. The recycled steel can be used back in the industry or else in other industries like utensils.
Mitsui Seiki: Mitsui Seiki has acquired a world-renowned reputation for producing the most accurate machine tools on earth. It uses nickel and titanium alloys for tis production. The scrap alloys can be used by RNR to recycle and be used back in the same or different industry.
Trans Tech: Trans Tech is a manufacturer of engineered electrical Power Transfer Systems. It uses stainless steel for its production. The scrap can be recycled and used back in the same company.
Wild Republic: Wild Republic is a leading manufacturer of interactive and innovative toys. It produces a lot of scrap plastic. If left in the disposal it will create a lot of environmental problems. RNR recycles the scrap plastic and it is used for packaging in all the 4 collaborating companies.
o Analysis of what I have calculated
The company has used one chart for variables and two for attributes. The p and c charts are the attributes charts. The control chart and the p chart were in control. The process in the c chart was not in control. It was brought in control by rejecting a few out of control samples and by implementing the corrective and prevention action plan.
· Summary
The process control charts help us in finding any process which is out of control. It helps us to monitor all the processes continuously and find any nuances in them. This helps in maintaining the quality of the products produced. Any process which is out of control can be brought in control by rejecting a few samples and implementing the corrective and preventive actions plan.
· Further work needed/proposed
RNR should further help the collaborating companies to implements these statistical methods. These control charts will even help the other companies to maintain quality in the company. Continuous monitoring and control over the processes is very important.
· References
ü Ranky P.G. PhD, IE 673 Total Quality Management Fall 2011 eLearning Pack ID: IE673-Fall2011-60-35
ü http://www.cimwareukandusa.com/All-Green/Ranky-GreenEngineering.html
ü http://www.transtech.com/index.php
ü http://www.contrinex.ch/defaultCountry.asp?idSite=101&langage=1
ü http://www.wildrepublic.com/WildRepDefault.aspx
